nearest
In [1]:
'''
An example showing nearest point queries,
primitive volume sampling, oriented bounding boxes,
and using PointCloud objects for visualization
'''
import trimesh
import numpy as np
In [2]:
# load a large- ish PLY model with colors
mesh = trimesh.load('../models/cycloidal.ply')
In [3]:
# we can sample the volume of Box primitives
points = mesh.bounding_box_oriented.sample_volume(count=10)
In [4]:
# find the closest point on the mesh to each random point
(closest_points,
distances,
triangle_id) = mesh.nearest.on_surface(points)
print('Distance from point to surface of mesh:\n{}'.format(distances))
Distance from point to surface of mesh: [0.00335445 0.08106453 0.18163656 0.35840587 0.09608193 0.00948973 0.04144551 0.09548092 0.11829215 0.00486006]
In [5]:
# create a PointCloud object out of each (n,3) list of points
cloud_original = trimesh.points.PointCloud(points)
cloud_close = trimesh.points.PointCloud(closest_points)
# create a unique color for each point
cloud_colors = np.array([trimesh.visual.random_color() for i in points])
# set the colors on the random point and its nearest point to be the same
cloud_original.vertices_color = cloud_colors
cloud_close.vertices_color = cloud_colors
# create a scene containing the mesh and two sets of points
scene = trimesh.Scene([mesh,
cloud_original,
cloud_close])
# show the scene wusing
scene.show()
Out[5]: